Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add systempromptio/systemprompt-marketplace --skill feature-optimisergit clone --depth 1 https://github.com/systempromptio/systemprompt-marketplaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/systempromptio/systemprompt-marketplace/feature-optimiser)<a href="https://agentmods.dev/skills/systempromptio/systemprompt-marketplace/feature-optimiser"><img src="https://agentmods.dev/badge/skills/systempromptio/systemprompt-marketplace/feature-optimiser/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/systempromptio/systemprompt-marketplace/feature-optimiser"><img src="https://agentmods.dev/badge/skills/systempromptio/systemprompt-marketplace/feature-optimiser.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00218 | $0.15379 |
| Opus 5.5 | $0.00087 | $0.06152 |
| Sonnet 5.5 | $0.00044 | $0.03076 |
| Haiku 4.5 | $0.00022 | $0.01538 |
Grade A, and why
feature-optimiser scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
from urllib.request import urlopen, Request The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 940 lines · 218 tokens per session scan A 8049ee80e4e3
feature-optimiser is a skill published in the GitHub repository systempromptio/systemprompt-marketplace (2 stars, last pushed 19d ago), with no licence file. It adds 218 tokens to every session and 15,379 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
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audit-encapsulation
Audit skill-encapsulation violations; remediation runs only behind an explicit fix FILE:LINE. External citations reaching into private surfaces inside .claude/skills/X/ or plugins//skills/X/ (marketplace monorepos) beyond the slash invocation. Use when: 'audit encapsulation', 'find skill leaks', 'skill boundary…
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Deduplicate repeated markdown (rule files, skill bodies, ADRs, docs) into one named source of truth and migrate every call site to cite it by exact heading. Use when the same prose, literal, or concept recurs across files: 'DRY this prose', 'extract a shared rule', 'single source of truth for X', a value-bump diff…
audit-progressive-disclosure
Read-only progressive-disclosure audit of agent-facing instruction markdown: grades each file against always-loaded, invocation-loaded, and on-demand tiers and flags split and disclosure defects. Use when: 'progressive disclosure', 'split this file', 'this file is too long', 'too much context', 'mixed concerns', 'hub…
generate
Build a source-backed AI industry briefing from official vendor publications, configured RSS feeds, GitHub releases, reputable secondary reporting, and user-supplied URLs. Use when: 'ai briefing', 'ai news', 'what's new in AI', 'catch me up on AI', 'prep for AI meeting', 'AI roundup', or 'generate AI slides'.
quiz-me
Post-work comprehension check: a self-contained HTML report of a completed change (context, intuition, decisions) with a quiz at the bottom that tests the human, not the artifact; also recalls prior quizzed work. Non-gating by default; the quizpolicy userConfig tunes offer cadence. Use when the user says 'quiz me'…